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Information from different bio-signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio-signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a representation of features extracted from handwriting and gait that can be used as a complement to speech-based features. Three different problems are addressed: classification of PD patients vs. healthy controls, prediction of the neurological state of PD patients according to the UP-DRS score, and the prediction of a modified version of the Frenchay dysarthria assessment (m-FDA). According to the results, the proposed approach is suitable to improve the results in the addressed problems, specially in the prediction of the UPDRS, and m-FDA scores.

In this paper, we apply the recently proposed x-vector neural network architecture for the task of age estimation. This architecture maps a variable length utterance into a fixed dimensional embedding which retains the relevant sequence level information. This is achieved by a temporal pooling layer. From the embedding, a series of layers is applied to make predictions. The full network is trained end-to-end in a discriminative fashion. This kind of network is starting to outperform the state-ofthe-art i-vector embeddings in tasks like speaker and language recognition. Motivated by this, we investigated the optimum way to train x-vectors for the age estimation task. Despite that a regression objective is typical for this task, we found that optimizing a mixture of classification and regression losses provides better results. We trained our models on the NIST SRE08 dataset and evaluated on SRE10. The proposed approach improved mean absolute error (MAE) by 12% w.r.t the i-vector baseline.

Speaker Verification still suffers from the challenge of generalization to novel adverse environments. We leverage on the recent advancements made by deep learning based speech enhancement and propose a feature-domain supervised denoising based solution. We propose to use Deep Feature Loss which optimizes the enhancement network in the hidden activation space of a pre-trained auxiliary speaker embedding network. We experimentally verify the approach on simulated and real data. A simulated testing setup is created using various noise types at different SNR levels. For evaluation on real data, we choose BabyTrain corpus which consists of children recordings in uncontrolled environments. We observe consistent gains in every condition over the state-of-the-art augmented Factorized-TDNN x-vector system. On BabyTrain corpus, we observe relative gains of 10.38% and 12.40% in minDCF and EER respectively.

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Current speaker recognition technology provides great performance with the x-vector approach. However, performance decreases when the evaluation domain is different from the training domain, an issue usually addressed with domain adaptation approaches. Recently, unsupervised domain adaptation using cycle-consistent Generative Adversarial Networks (CycleGAN) has received a lot of attention. Cycle-GAN learn mappings between features of two domains given non-parallel data. We investigate their effectiveness in low resource scenario i.e. when limited amount of target domain data is available for adaptation, a case unexplored in previous works. We experiment with two adaptation tasks: microphone to telephone and a novel reverberant to clean adaptation with the end goal of improving speaker recognition performance. Number of speakers present in source and target domains are 7000 and 191 respectively. By adding noise to the target domain during CycleGAN training, we were able to achieve better performance compared to the adaptation system whose CycleGAN was trained on a larger target data. On reverberant to clean adaptation task, our models improved EER by 18.3% relative on VOiCES dataset compared to a system trained on clean data. They also slightly improved over the state-of-the-art Weighted Prediction Error (WPE) de-reverberation algorithm.

In this paper, the NIST 2016 SRE system that resulted from the collaboration between MIT Lincoln Laboratory and the team at Johns Hopkins University is presented. The submissions for the 2016 evaluation consisted of three fixed condition submissions and a single system open condition submission. The primary submission on the fixed (and core) condition resulted in an actual DCF of .618. Details of the submissions are discussed along with some discussion and observations of the 2016 evaluation campaign.